An Efficient Game-Theoretic Planner for Automated Lane Merging with Multi-Modal Behavior Understanding

Conference Paper (2023)
Author(s)

Luyao Zhang (TU Delft - Team Sergio Grammatico)

S Han (Student TU Delft)

Sergio Grammatico (TU Delft - Team Bart De Schutter, TU Delft - Team Sergio Grammatico)

Research Group
Team Sergio Grammatico
DOI related publication
https://doi.org/10.1109/ITSC57777.2023.10422316
More Info
expand_more
Publication Year
2023
Language
English
Research Group
Team Sergio Grammatico
Pages (from-to)
3085-3090
ISBN (electronic)
979-8-3503-9946-2
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

In this paper, we propose a novel behavior planner that combines game
theory with search-based planning for automated lane merging.
Specifically, inspired by human drivers, we model the interaction
between vehicles as a gap selection process. To overcome the challenge
of multi-modal behavior exhibited by the surrounding vehicles, we
formulate the trajectory selection as a matrix game and compute an
equilibrium. Next, we validate our proposed planner in the high-fidelity
simulator CARLA and demonstrate its effectiveness in handling
interactions in dense traffic scenarios.

Files

An_Efficient_Game-Theoretic_Pl... (pdf)
(pdf | 4.31 Mb)
- Embargo expired in 13-08-2024
License info not available